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Abstract #3821

Accelerated Low-rank MPnRAGE Denoising and T1 Reconstruction using Jointly Trained U-Net Regularizers

Punnawish Thuwajit1, Kuan-fu Chen1, Jayse Merle Weaver1,2, Andrew L Alexander1,3, Douglas Dean III1,2,4, Kevin M Johnson2,5, and Steven R Kecskemeti1
1Waisman Center, University of Wisconsin-Madison, Madison, WI, United States, 2Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, 3Psychiatry, University of Wisconsin-Madison, Madison, WI, United States, 4Pediatrics, University of Wisconsin-Madison, Madison, WI, United States, 5Radiology, University of Wisconsin-Madison, Madison, WI, United States

Synopsis

Keywords: Quantitative Imaging, Image Reconstruction

Motivation: Quantitative T1 (qT1) using MPnRAGE is a promising brain biomarker. However, reliable qT1 measurements at 1 mm isotropic resolution across the brain may require long scan times (>8min).

Goal(s): We aim to develop a low-rank denoising strategy providing reliable qT1 estimations from accelerated scans in about 2 minutes.

Approach: We developed a novel strategy using two U-Net models, the denoiser and T1-estimator, trained together to jointly convert accelerated low-rank scans into accurate qT1 maps.

Results: Our method exhibits good bias correction with low errors in both gray matter and white matter (<3%) with high image acceleration.

Impact: Our method provides fast and accurate whole-brain high-resolution qT1 estimation from MPnRAGE scans in about 2 minutes.

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Keywords